Quantifying the effect of in-hospital antimicrobial use on the development of colistin-resistant Acinetobacter baumannii strains: a time series analysis.

OBJECTIVES: Colistin is currently considered an essential therapeutic option for the treatment of hospital-acquired infections caused by resistant isolates of Acinetobacter baumannii. In this study, conducted in a tertiary care hospital, the effect of previous detection rates and antimicrobial use on colistin-resistant A. baumannii strains was investigated. METHODS: Susceptibility data for A. baumannii isolates and colistin use information were collected for 48 consecutive months (January 2014 to December 2017). ARIMA models were used to explore the time series of colistin use and resistance to A. baumannii. In addition, dynamic regression models were used to study the relationships between the use of antimicrobials (colistin, imipenem, meropenem, cefepime, ciprofloxacin) and colistin resistance. RESULTS: The results of the univariate model showed a statistically significant positive association between colistin use and the detection rate of colistin-resistant A. baumannii (p=0.003). Moreover, a multivariate model confirmed the positive association of colistin use with the detection rate of colistin-resistant A. baumannii, also demonstrating statistically significant negative associations with imipenem (p=0.004) and meropenem use (p=0.005). CONCLUSIONS: This study quantifies the effect of colistin use on the development of resistant strains. These findings can assist antimicrobial stewardship teams to elaborate their plans and predict the effect of their interventions.

Correlation between Acinetobacter baumannii Resistance and Hospital Use of Meropenem, Cefepime, and Ciprofloxacin: Time Series Analysis and Dynamic Regression Models.

Acinetobacter baumannii is one of the most difficult-to-treat pathogens worldwide, due to developed resistance. The aim of this study was to evaluate the use of widely prescribed antimicrobials and the respective resistance rates of A. baumannii, and to explore the relationship between antimicrobial use and the emergence of A. baumannii resistance in a tertiary care hospital. Monthly data on A. baumannii susceptibility rates and antimicrobial use, between January 2014 and December 2017, were analyzed using time series analysis (Autoregressive Integrated Moving Average (ARIMA) models) and dynamic regression models. Temporal correlations between meropenem, cefepime, and ciprofloxacin use and the corresponding rates of A. baumannii resistance were documented. The results of ARIMA models showed statistically significant correlation between meropenem use and the detection rate of meropenem-resistant A. baumannii with a lag of two months (p = 0.024). A positive association, with one month lag, was identified between cefepime use and cefepime-resistant A. baumannii (p = 0.028), as well as between ciprofloxacin use and its resistance (p < 0.001). The dynamic regression models offered explanation of variance for the resistance rates (R2 > 0.60). The magnitude of the effect on resistance for each antimicrobial agent differed significantly.

Human subcutaneous dirofilariasis caused by Dirofilaria immitis in a Greek adult.

Human dirofilariasis is a zoonotic infection caused by worms of the genus Dirofilaria. Most reported cases involve Dirofilaria repens, and D. immitis infection has been rarely reported. Canines act as a reservoir for the infection, while human infections are sporadic. Human dirofilariasis has been widely reported in South Europe; however, the worldwide distribution constantly changes. We herein report an extremely rare case of subcutaneous trunk dirofilariasis in a 45-year-old immunosuppressed woman, caused by D. immitis. The parasitic infection was detected using ultrasonography. The infection was confirmed by a polymerase chain reaction-based method and was attributed to D. immitis.